Fighting Cancer with Machine learning
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Research Focus

We are building the Cancer Dependency Map!

Precision cancer medicine

We build predictive models to predict cancer vulnerabilities from genomic profiles of tumors and cancer cell lines.

Cancer Targets Identification

We integrate functional screening and 'omics data to identify novel cancer targets as well as drugs for repurposing.


We develop computational methods and tools to facilitate the analysis of CRISPR screening in cancer models.

Small-molecule screens

We analyze highly-multiplexed small-molecule screening data from the PRISM platform to discover novel cancer therapeutic leads.

Our Team

Aviad Tsherniak

Associate Director

Philip Montgomery

Sr Principal Software Engineer

Mike Burger

Associate Computational Biologist II

Neekesh Dharia

Postdoctoral Scholar

James McFarland

Data Scientist II

Josephine Lee

Software Engineer

Josh Dempster

Data Scientist

Guillaume Kugener

Associate Computational Biologist I

Zandra Ho

Associate Computational Biologist I

Jordan Rossen

Associate Computational Biologist I

Allie Warren

Associate Computational Biologist I

Andrew Tang

Sr. Visual Designer

Mustafa Kocak

Computational Scientist I

Andy Jones

Associate Computational Biologist I

Phoebe Moh

Associate Software Engineer

Mariya Kazachkova

Associate Computational Biologist I

Vickie Wang

Associate Computational Biologist I

Josh Pan

Postdoctoral fellow

Yejia Chen

Software Engineer

Jérémie Kalfon

Computational Associate I

Join Us

Associate Computational Biologist, Target Discovery

Apply here

A major obstacle for treating cancer is a lack of precision medicines. Many potential targeted therapies fail to transition from preclinical models to patients due to incomplete knowledge of the drug’s mechanism of action and/or absence of robust biomarkers to identify relevant patient populations. The Target Discovery arm of the Cancer Dependency Map project aims to provide the oncology community with potential drug targets that have a high likelihood of success.

Our strategy is to develop a more comprehensive understanding of each target’s function in cancer by performing computational analyses that establish associations between the target and the molecular features that predict sensitivity to its perturbation. This information helps guide us in nominating targets that are most likely to translate to patient tumors.

You will be responsible for improving the computational framework for systematically identifying and prioritizing cancer-related gene targets. As part of this effort, you will work with some of the largest experimental cancer biology datasets in the world, including functional genomic (CRISPR, RNAi) and small molecule screens on hundreds of cancer cell lines, and their corresponding multi-omics profiles, e.g. RNA-Seq, whole exome sequencing, and methylation. Experience with software pipeline development is a plus.

Associate Computational Biologist, Flagship

Apply here

As part of the Dependency Map project, several large-scale ‘Flagship’ efforts are now underway. These highly collaborative projects seek to combine insights from large-scale preclinical datasets of cancer vulnerabilities with the expertise of clinician scientists in particular disease areas (e.g. pediatric cancer, GI cancer).

As part of the Flagship DepMap teams, you will work closely with cancer biologists and clinicians to derive new translational insights from large omics and functional screening datasets. These projects will require you to help design experimental and computational research strategies, analyze a broad range of complex, high-dimensional data, and apply a variety of statistical and machine learning tools.


Han Xu

Associate Professor, MD Anderson

Robin Meyers

Graduate Student, Genetics, Stanford University

Jared Jacobsen

Studying for AI Research

Li Wang

Computational Biologist, 10X Genomics

Jordan Bryan

Graduate Student, Statistics, Duke University

Kailash Nakagawa

Student, Cambridge Rindge and Latin School

Quinton Wessells

Graduate Student, Biomedical Informatics, Stanford University

Remi Marenco

Bioinformation Lead, Cancer Cell Line Factory

Selected publications

Contact Us

Aviad Tsherniak

Cancer Data Science
Broad Institute of MIT and Harvard
415 Main Street
Cambridge, MA 02142

Email: [first name] at